Dear Colleagues,

In recent years, the capabilities and applications of advanced geospatial technologies—such as 3D laser scanning (i.e., lidar), structure from motion, multi-view stereo, photogrammetry, etc.—to support the spatio-temporal monitoring of the natural and built environment have exploded. These systems have become more portable, flexible, faster, and produce hyperspatial (sub-meter) point cloud data of higher quality in terms of resolution and precision. Example applications include geohazards (e.g., landslides, rockfall, seismic/tectonics, glacial degradation, and coastal erosion), ecosystems and biodiversity (e.g., forest biomass, post-fire regrowth, and habitat), infrastructure condition monitoring, and structural heath monitoring.   

The assortment of sensors used for these purposes have diverse specifications for range, resolution, and accuracy. While these systems provide data of high quality in terms of measurement precision and resolution, there are many challenges if applying these systems to monitoring applications. First, point density and occlusions can vary substantially across the datasets, resulting in difficulties applying processing workflows developed for other remote sensing technologies that produce more uniform and consistent datasets. Second, these systems rapidly produce immense amounts of data that often need to be aggressively downsampled in order to be utilized in the conventional analysis programs specific to many of the applications; this constrains the ability to detect small trends and subtle changes. Third, many analysis algorithms are not suited to handle the rich 3D geometric data provided by these sensors and often reduce the data to 2D, which can result in distortions. Further, a variety of workflows are used for different stages of data processing that can result in systematic biases in the data. Lastly, the data quality can vary substantially with the sensors utilized, and the georeferencing methods employed and monitoring results are highly dependent on rigorous geodetic control and procedures. These challenges significantly affect the ability to reliably use point clouds for monitoring applications. Further, the high processing burden can limit the timeliness and value of the monitoring information provided in point clouds.

Fortunately, many promising solutions are emerging through point cloud research in the various communities utilizing these sensors for monitoring. Another key opportunity and challenge lies in the versatility of the technologies being utilized by a wide range of communities for different monitoring applications. As a result, research developing techniques and validating them through case studies are scattered across these disciplines and often this information is redeveloped by other communities. However, this versatility presents a unique opportunity to synthesize and integrate these experiences and expertise across these disciplines as a broader geospatial community.

To this end, this Special Issue promotes new and innovative field procedures, data acquisition techniques, data processing and analysis algorithms to support monitoring, combined sensor or geospatial data integration, and uncertainty modelling for improved monitoring with point clouds. We invite submissions of either original technical papers or high-quality review papers that shed new light on a particular perspective of geospatial monitoring with point clouds. Contributions that develop techniques relevant to monitoring (e.g., point cloud classification) are welcome, but should provide a clear application to monitoring rather than presenting a generic approach. Likewise, monitoring applications that do not utilize a point cloud in some form will not be considered for the Special Issue. We encourage you to participate in this important Special Issue and hope to see your contribution!

Assoc. Prof. Michael James Olsen
Assoc. Prof. Michael Starek
Assoc. Prof. Craig Glennie
Guest Editors

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• Hazard monitoring such as earthquakes/tectonics, hurricanes, landslides, coastal change, ecosystems, etc
• Post-disaster reconnaissance and forensic investigations
• Infrastructure condition assessment and monitoring
• Ecosystem and habitat monitoring
• Traffic monitoring
• Autonomous systems for novel 3D monitoring applications
• Structural health monitoring with geospatial technologies
• Multi-sensor fusion (e.g., unmanned aircraft systems, UAS, SfM photogrammetry combined with terrestrial laser scanning)
• Novel surveying and geodetic control procedures to support monitoring applications
• Point cloud data to validate numerical/analytical modeling of deformation
• Deformation/change detection and analysis algorithms and techniques
• Uncertainty modeling, particularly in terms of the uncertainty present in the deformation analysis when comparing multiple epochs of data
• 3D data processing and machine learning for point cloud classification relevant to monitoring